Add OpenFlamingo

requirements.txt

@@ -30,6 +30,10 @@ certifi==2024.2.2
 cffi==1.16.0
 cfgv==3.4.0
 charset-normalizer==2.1.1
+Cython==0.29.32
+einops-exts==0.0.4
+emoji==2.1.0
+et-xmlfile==1.1.0
 chex==0.1.7
 click==8.1.7
 clip-anytorch==2.5.2
@@ -145,6 +149,10 @@ nltk==3.8.1
 nodeenv==1.8.0
 NudeNet==2.0.9
 numba==0.56.4
+open-clip-torch==2.24.0
+openpyxl==3.0.10
+outcome==1.2.0
+pathy==0.10.2
 numpy==1.23.5
 oauthlib==3.2.2
 omegaconf==2.3.0
@@ -253,6 +261,15 @@ timm==0.6.13
 tokenizers==0.15.2
 toml==0.10.2
 tomli==2.0.1
+trio==0.22.0
+trio-websocket==0.9.2
+types-Pillow==9.3.0.4
+types-pytz==2022.4.0.0
+types-redis==4.3.21.1
+types-requests==2.28.11.2
+types-tabulate==0.9.0.0
+types-urllib3==1.26.25
+typing==3.7.4.3
 toolz==0.12.1
 torch~=2.1.2
 torch-fidelity==0.3.0
@@ -281,3 +298,4 @@ xxhash==3.4.1
 yarl==1.9.4
 zipp==3.17.0
 zstandard==0.18.0
+fairlearn==0.9.0

setup.cfg

@@ -148,9 +148,14 @@ models =
     crfm-helm[yandex]
 
 vlm =
+    # For OpenFlamingo
+    einops~=0.7.0
+    einops-exts~=0.0.4
+    open-clip-torch~=2.24.0
+
     # VLM models
     crfm-helm[openai]
-    torch~=2.1.2    # For IDEFICS
+    torch~=2.1.2 # For IDEFICS
 
     # VLM scenarios
     crfm-helm[images]
@@ -178,7 +183,7 @@ heim =
     crfm-helm[openai]
 
     # For model, kakaobrain/mindall-e
-    einops~=0.6.0
+    einops~=0.7.0
     omegaconf~=2.3.0
     pytorch-lightning~=2.0.5
 
@@ -259,6 +264,7 @@ exclude =
     venv/*
     src/helm/clients/image_generation/dalle_mini/*
     src/helm/clients/image_generation/mindalle/*
+    src/helm/clients/vision_language/open_flamingo/*
 
 # Ignore completely:
 # E203 - White space before ':', (conflicts with black)
@@ -276,7 +282,7 @@ check_untyped_defs = True
 disable_error_code = annotation-unchecked
 # TODO: Change disallow_untyped_defs to True
 disallow_untyped_defs = False
-exclude = dalle_mini|mindalle
+exclude = dalle_mini|mindalle|open_flamingo
 
 [tool:pytest]
 addopts =

src/helm/benchmark/model_metadata_registry.py

@@ -61,13 +61,17 @@
 IDEFICS_MODEL_TAG: str = "IDEFICS_MODEL_TAG"
 # Llava should use a special prompt format (see `LlavaRunExpander`)
 LLAVA_MODEL_TAG: str = "LLAVA_MODEL_TAG"
+# OpenFlamingo has a special prompt format (see `OpenFlamingoRunExpander`)
+OPEN_FLAMINGO_MODEL_TAG: str = "OPEN_FLAMINGO_MODEL_TAG"
 # Some VLMs do not support multiple images in the prompt
 LIMITED_FUNCTIONALITY_VLM_TAG: str = "LIMITED_FUNCTIONALITY_VLM_TAG"
 FULL_FUNCTIONALITY_VLM_TAG: str = "FULL_FUNCTIONALITY_VLM_TAG"
 
 
 # Frozen is set to false as the model_deployment_registry.py file
 # might populate the deployment_names field.
+
+
 @dataclass(frozen=False)
 class ModelMetadata:
     name: str

src/helm/benchmark/presentation/run_specs_image2structure.conf

@@ -10,6 +10,11 @@ entries: [
     # sheetmusic2lilypond
     {description: "sheetmusic2lilypond:model=vlm", priority: 1}
 
+    # webpages
+    {description: "image2webpage:subset=css,model=vlm", priority: 1, groups: ["image2webpage"]}
+    {description: "image2webpage:subset=html,model=vlm", priority: 1, groups: ["image2webpage"]}
+    {description: "image2webpage:subset=javascript,model=vlm", priority: 1, groups: ["image2webpage"]}
+
     # chart2csv
     # {description: "chart2csv:model=vlm", priority: 1}
 ]

src/helm/benchmark/run_expander.py

@@ -447,6 +447,26 @@ def expand(self, run_spec: RunSpec) -> List[RunSpec]:
         ]
 
 
+class OpenFlamingoRunExpander(RunExpander):
+    """
+    Custom prompt for OpenFlamingo following: https://huggingface.co/openflamingo/OpenFlamingo-9B-vitl-mpt7b
+    """
+
+    name = "open_flamingo"
+
+    def expand(self, run_spec: RunSpec) -> List[RunSpec]:
+        return [
+            replace(
+                run_spec,
+                name=run_spec.name,
+                adapter_spec=replace(
+                    run_spec.adapter_spec,
+                    input_prefix=f"<|endofchunk|>{run_spec.adapter_spec.input_prefix}",
+                ),
+            ),
+        ]
+
+
 class FormatPromptRunExpander(RunExpander):
     """Adds a prefix and suffix to the prompt."""
 

src/helm/benchmark/run_spec_factory.py

@@ -17,6 +17,7 @@
     GOOGLE_PALM_2_MODEL_TAG,
     IDEFICS_INSTRUCT_MODEL_TAG,
     LLAVA_MODEL_TAG,
+    OPEN_FLAMINGO_MODEL_TAG,
     NLG_PREFIX_TAG,
     NO_NEWLINES_TAG,
     OPENAI_CHATGPT_MODEL_TAG,
@@ -33,6 +34,7 @@
     IDEFICSInstructRunExpander,
     IncreaseTemperatureRunExpander,
     LlavaRunExpander,
+    OpenFlamingoRunExpander,
     OpenAIRunExpander,
     MistralRunExpander,
     StopRunExpander,
@@ -147,6 +149,10 @@ def alter_run_spec(run_spec: RunSpec) -> RunSpec:
         if LLAVA_MODEL_TAG in model.tags:
             run_spec = singleton(LlavaRunExpander().expand(run_spec))
 
+        # OpenFlamingo
+        if OPEN_FLAMINGO_MODEL_TAG in model.tags:
+            run_spec = singleton(OpenFlamingoRunExpander().expand(run_spec))
+
         # For multiple choice
         if BUGGY_TEMP_0_TAG in model.tags and run_spec.adapter_spec.temperature == 0:
             increase_temperature_expander = IncreaseTemperatureRunExpander(value=1e-4)

src/helm/clients/vision_language/open_flamingo/__init__.py

@@ -0,0 +1,2 @@
+from .src.flamingo import Flamingo
+from .src.factory import create_model_and_transforms

src/helm/clients/vision_language/open_flamingo/src/factory.py

@@ -0,0 +1,147 @@
+"""
+Source: https://github.com/mlfoundations/open_flamingo
+"""
+
+from typing import Optional
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from helm.common.general import handle_module_not_found_error
+from .flamingo import Flamingo
+from .flamingo_lm import FlamingoLMMixin
+from .utils import extend_instance
+
+
+def create_model_and_transforms(
+    clip_vision_encoder_path: str,
+    clip_vision_encoder_pretrained: str,
+    lang_encoder_path: str,
+    tokenizer_path: str,
+    cross_attn_every_n_layers: int = 1,
+    use_local_files: bool = False,
+    decoder_layers_attr_name: str = None,
+    freeze_lm_embeddings: bool = False,
+    cache_dir: Optional[str] = None,
+    **flamingo_kwargs,
+):
+    """
+    Initialize a Flamingo model from a pretrained vision encoder and language encoder.
+    Appends special tokens to the tokenizer and freezes backbones.
+
+    Args:
+        clip_vision_encoder_path (str): path to pretrained clip model (e.g. "ViT-B-32")
+        clip_vision_encoder_pretrained (str): name of pretraining dataset for clip model (e.g. "laion2b_s32b_b79k")
+        lang_encoder_path (str): path to pretrained language encoder
+        tokenizer_path (str): path to pretrained tokenizer
+        cross_attn_every_n_layers (int, optional): determines how often to add a cross-attention layer. Defaults to 1.
+        use_local_files (bool, optional): whether to use local files. Defaults to False.
+        decoder_layers_attr_name (str, optional): name of the decoder layers attribute. Defaults to None.
+        freeze_lm_embeddings (bool, optional): whether to freeze LM input embeddings when configuring Perceiver.
+        cache_dir (str, optional): path to cache directory for downloading OpenClip/HF weights.
+    Returns:
+        Flamingo: Flamingo model from pretrained vision and language encoders
+        Image processor: Pipeline to preprocess input images
+        Tokenizer: A tokenizer for the language model
+    """
+    try:
+        import open_clip
+    except ModuleNotFoundError as e:
+        handle_module_not_found_error(e, ["vlm"])
+
+    vision_encoder, _, image_processor = open_clip.create_model_and_transforms(
+        clip_vision_encoder_path,
+        pretrained=clip_vision_encoder_pretrained,
+        cache_dir=cache_dir,
+    )
+    # set the vision encoder to output the visual features
+    vision_encoder.visual.output_tokens = True
+
+    text_tokenizer = AutoTokenizer.from_pretrained(
+        tokenizer_path,
+        local_files_only=use_local_files,
+        trust_remote_code=True,
+        cache_dir=cache_dir,
+    )
+    # add Flamingo special tokens to the tokenizer
+    text_tokenizer.add_special_tokens({"additional_special_tokens": ["<|endofchunk|>", "<image>"]})
+    if text_tokenizer.pad_token is None:
+        # Issue: GPT models don't have a pad token, which we use to
+        # modify labels for the loss.
+        text_tokenizer.add_special_tokens({"pad_token": "<PAD>"})
+
+    lang_encoder = AutoModelForCausalLM.from_pretrained(
+        lang_encoder_path,
+        local_files_only=use_local_files,
+        trust_remote_code=True,
+        cache_dir=cache_dir,
+    )
+
+    # hacks for MPT-1B, which doesn't have a get_input_embeddings method
+    if "mpt-1b-redpajama-200b" in lang_encoder_path:
+
+        class EmbeddingFnMixin:
+            def get_input_embeddings(self):
+                return self.transformer.wte
+
+            def set_input_embeddings(self, new_embeddings):
+                self.transformer.wte = new_embeddings
+
+        extend_instance(lang_encoder, EmbeddingFnMixin)
+
+    # convert LM to FlamingoLM
+    extend_instance(lang_encoder, FlamingoLMMixin)
+
+    if decoder_layers_attr_name is None:
+        decoder_layers_attr_name = _infer_decoder_layers_attr_name(lang_encoder)
+    lang_encoder.set_decoder_layers_attr_name(decoder_layers_attr_name)
+    lang_encoder.resize_token_embeddings(len(text_tokenizer))
+
+    model = Flamingo(
+        vision_encoder,
+        lang_encoder,
+        text_tokenizer.encode("<|endofchunk|>")[-1],
+        text_tokenizer.encode("<image>")[-1],
+        vis_dim=open_clip.get_model_config(clip_vision_encoder_path)["vision_cfg"]["width"],
+        cross_attn_every_n_layers=cross_attn_every_n_layers,
+        **flamingo_kwargs,
+    )
+
+    # Freeze all parameters
+    model.requires_grad_(False)
+    assert sum(p.numel() for p in model.parameters() if p.requires_grad) == 0
+
+    # Unfreeze perceiver, gated_cross_attn_layers, and LM input embeddings
+    model.perceiver.requires_grad_(True)
+    model.lang_encoder.gated_cross_attn_layers.requires_grad_(True)
+    if not freeze_lm_embeddings:
+        model.lang_encoder.get_input_embeddings().requires_grad_(True)
+        # TODO: investigate also training the output embeddings when untied
+
+    print(
+        f"Flamingo model initialized with {sum(p.numel() for p in model.parameters() if p.requires_grad)} trainable parameters"
+    )
+
+    return model, image_processor, text_tokenizer
+
+
+def _infer_decoder_layers_attr_name(model):
+    for k in __KNOWN_DECODER_LAYERS_ATTR_NAMES:
+        if k.lower() in model.__class__.__name__.lower():
+            return __KNOWN_DECODER_LAYERS_ATTR_NAMES[k]
+
+    raise ValueError(
+        "We require the attribute name for the nn.ModuleList in the decoder storing the transformer block layers. "
+        "Please supply this string manually."
+    )
+
+
+__KNOWN_DECODER_LAYERS_ATTR_NAMES = {
+    "opt": "model.decoder.layers",
+    "gptj": "transformer.h",
+    "gpt-j": "transformer.h",
+    "pythia": "gpt_neox.layers",
+    "llama": "model.layers",
+    "gptneoxforcausallm": "gpt_neox.layers",
+    "mpt": "transformer.blocks",
+    "mosaicgpt": "transformer.blocks",
+}